On the Role of Artificial Intelligence in Human-Machine Symbiosis

arXiv cs.AI / 5/4/2026

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Key Points

  • The paper argues that human–machine symbiosis makes it hard to define what “AI-generated” information means, because content can emerge from mutual shaping rather than either side alone.
  • It focuses on tracing the functional role AI plays in natural language generation, noting that role signals in prompts can become unobservable when only the output text is available.
  • The authors propose a methodology that infers a latent role from the prompt, embeds that role during probabilistic generation, and then recovers the nature of AI participation from the resulting text.
  • Experiments consider two roles for AI—an assistive editor of human-written content and a creative agent generating from a short concept—and the results show the approach can distinguish roles, remain robust to perturbations, and preserve linguistic quality.
  • The work aims to support future research into the ethics of AI, including whether AI participation is fair, transparent, and appropriate.

Abstract

The evolution of artificial intelligence (AI) has rendered the boundary between humanity and computational machinery increasingly ambiguous. In the presence of more interwoven relationships within human-machine symbiosis, the very notion of AI-generated information becomes difficult to define, as such information arises not from either humans or machines in isolation, but from their mutual shaping. Therefore, a more pertinent question lies not merely in whether AI has participated, but in how it has participated. In general, the role assumed by AI is often specified, either implicitly or explicitly, in the input prompt, yet becomes less apparent or altogether unobservable when the generated content alone is available. Once detached from the dialogue context, the functional role may no longer be traceable. This study considers the problem of tracing the functional role played by AI in natural language generation. A methodology is proposed to infer the latent role specified by the prompt, embed this role into the content during the probabilistic generation process and subsequently recover the nature of AI participation from the resulting text. Experimentation is conducted under a representative scenario in which AI acts either as an assistive agent that edits human-written content or as a creative agent that generates new content from a brief concept. The experimental results support the validity of the proposed methodology in terms of discrimination between roles, robustness against perturbations and preservation of linguistic quality. We envision that this study may contribute to future research on the ethics of AI with regard to whether AI has been used fairly, transparently and appropriately.